Research Engineer / Research Scientist - Foundations Retrieval Lead

About Our TeamJoin the Foundations Research team, where we tackle ambitious and innovative projects that could redefine the future of AI. Our mission is to enhance the science behind our training and scaling initiatives, focusing on pioneering frontier models. We are dedicated to advancing data utilization, scaling methodologies, optimization strategies, model architectures, and efficiency enhancements to accelerate our scientific breakthroughs.About the PositionWe are on the lookout for a dynamic technical research lead to spearhead our embeddings-focused retrieval initiatives. You will oversee a talented team of research scientists and engineers committed to developing foundational technologies that enable models to access and utilize the right information precisely when needed. This includes crafting innovative embedding training objectives, architecting scalable vector storage, and implementing adaptive indexing techniques.This pivotal role will contribute to various OpenAI products and internal research initiatives, offering opportunities for scientific publication and significant technical influence.This position is located in San Francisco, CA, where we embrace a hybrid work model, requiring three days in the office weekly, and we provide relocation assistance for new hires.Your ResponsibilitiesLead cutting-edge research on embedding models and retrieval systems optimized for grounding, relevance, and adaptive reasoning.Supervise a team of researchers and engineers in building an end-to-end infrastructure for training, evaluating, and integrating embeddings into advanced models.Drive advancements in dense, sparse, and hybrid representation techniques, metric learning, and retrieval systems.Work collaboratively with Pretraining, Inference, and other Research teams to seamlessly integrate retrieval throughout the model lifecycle.Contribute to OpenAI's ambitious vision of developing AI systems with robust memory and knowledge access capabilities rooted in learned representations.You Will Excel in This Role If You PossessA proven track record of leading high-performance teams of researchers or engineers within ML infrastructure or foundational research.In-depth technical knowledge in representation learning, embedding models, or vector retrieval systems.Familiarity with transformer-based large language models and their interaction with embedding spaces and objectives.Research experience in areas such as contrastive learning and retrieval-augmented generation.

About the Foundations Retrieval Team The Foundations Research group at OpenAI explores new approaches that could shape artificial intelligence for years to come. The team focuses on improving the science and data behind model training and scaling, especially for future advanced models. Areas of focus include data utilization, scaling laws, optimization strategies, model architectures, and efficiency improvements. Within Foundations, the Search team builds agentic search solutions. This group works closely with others to design interfaces between models and the core search stack, serving, indexing, and retrieval, so model intent leads to reliable, real-world results. The team develops large-scale systems to transform and index massive information sources, enabling models to reason over global knowledge. Close collaboration with researchers helps move new modeling ideas into production quickly, changing how intelligent systems discover and synthesize information at scale. Role Overview OpenAI is hiring a Software Engineer with expertise in retrieval system development and scalability for its San Francisco office. This role involves working with researchers and engineers to build infrastructure that lets models access the right information when needed. Responsibilities include designing and operating indexing systems, retrieval pipelines, and serving layers. Work in this role will directly improve retrieval capabilities across OpenAI’s research and products, with a strong influence on system performance, reliability, and scalability. What You’ll Do Develop and scale retrieval infrastructure, including indexing, serving, and query execution. Build low-latency, high-throughput systems for real-time model interactions. Work with research teams to bring embedding and retrieval methods into production. Support dense, sparse, and hybrid retrieval pipelines. Maintain system performance, reliability, and observability at scale. Collaborate with Pretraining, Inference, and Product teams to deliver end-to-end retrieval solutions. Help develop model-system interfaces for agentic workflows. Who We’re Looking For Experience building and scaling distributed systems. Background in developing high-performance, low-latency systems. Hands-on work with indexing and retrieval techniques. Familiarity with hybrid retrieval systems. Comfort working collaboratively across multiple teams.

About the TeamJoin the innovative Post-Training team at OpenAI, where we focus on refining and elevating pre-trained models for deployment in ChatGPT, our API, and future products. Collaborating closely with various research and product teams, we conduct crucial research that prepares our models for real-world deployment to millions of users, ensuring they are safe, efficient, and reliable.About the RoleAs a Research Engineer / Scientist, you will spearhead the research and development of enhancements to our models. Our work intersects reinforcement learning and product development, aiming to create cutting-edge solutions.We seek passionate individuals with robust machine learning engineering skills and research experience, particularly with innovative and powerful models. The ideal candidate will be driven by a commitment to product-oriented research.This position is located in San Francisco, CA, and follows a hybrid work model requiring three days in the office each week. Relocation assistance is available for new employees.In this role, you will:Lead and execute a research agenda aimed at enhancing model capabilities and performance.Work collaboratively with research and product teams to empower customers to optimize their models.Develop robust evaluation frameworks to monitor and assess modeling advancements.Design, implement, test, and debug code across our research stack.You may excel in this role if you:Possess a deep understanding of machine learning and its applications.Have experience with relevant models and methodologies for evaluating model improvements.Are adept at navigating large ML codebases for debugging purposes.Thrive in a fast-paced and technically intricate environment.About OpenAIOpenAI is a pioneering AI research and deployment organization dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We are committed to pushing the boundaries of AI capabilities while prioritizing safety and human-centric values in our products. Our mission is to embrace diverse perspectives, voices, and experiences that represent the full spectrum of humanity, as we strive for a future where AI is a powerful ally for everyone.

About Our TeamJoin the forefront of AI innovation with the RL and Reasoning team at OpenAI. Our team is dedicated to advancing reinforcement learning research and has pioneered transformative projects, including o1 and o3. We are committed to pushing the limits of generative models while ensuring their scalable deployment.About the RoleAs a Research Engineer/Research Scientist at OpenAI, you will play a pivotal role in enhancing AI alignment and capabilities through state-of-the-art reinforcement learning techniques. Your contributions will be essential in training intelligent, aligned, and versatile agents that power various AI models.We seek individuals with a solid foundation in reinforcement learning research, agile coding skills, and a passion for rapid iteration.This position is located in San Francisco, CA, and follows a hybrid work model of three days in the office per week. We also provide relocation assistance for new hires.You may excel in this role if:You are enthusiastic about being at the cutting edge of RL and language model research.You take initiative, owning ideas and driving them to fruition.You value principled methodologies, conducting simple experiments in controlled environments to draw trustworthy conclusions.You thrive in a fast-paced, complex technical environment where rapid iteration is essential.You are adept at navigating extensive ML codebases to troubleshoot and enhance them.You possess a profound understanding of machine learning and its applications.About OpenAIOpenAI is a pioneering AI research and deployment organization committed to ensuring that general-purpose artificial intelligence serves the greater good for humanity. We strive to push the boundaries of AI system capabilities while prioritizing safe deployment through our innovative products. We recognize AI as a powerful tool that must be developed with safety and human-centric principles, embracing diverse perspectives to reflect the full spectrum of humanity.We are proud to be an equal opportunity employer, welcoming applicants from all backgrounds without discrimination based on race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or any other legally protected characteristic.

About Our TeamAt OpenAI, we are dedicated to extending the reach of our advanced AI technology through innovative products like ChatGPT and the OpenAI API. Our mission is to learn from deployment and share the benefits of AI, while prioritizing safety and responsible usage over unchecked growth.Role OverviewWe are on the lookout for a seasoned Research Engineer to spearhead efforts in retrieval and search across our API and ChatGPT platforms. As the AI landscape continues to evolve, retrieval and search capabilities have emerged as pivotal for our models. You will play a crucial role in developing search-based product experiences that will impact millions of users globally.In this position, your responsibilities will include:Collaborating closely with our research team to advance retrieval and search algorithms across various domains such as document search, enterprise search, knowledge retrieval, and web-scale search.Implementing these search methodologies into production for both the API and ChatGPT, enhancing user experiences for millions.Investigating cutting-edge research topics in retrieval and search to inform our product strategy for the future.Working together with researchers, engineers, product managers, and designers to introduce new features and innovations.Ideal Candidate ProfileYou will thrive in this role if you:Possess substantial experience in building and maintaining production-level machine learning systems.Have a background in working with vector databases, search indices, or other data storage solutions tailored for search and retrieval applications.Are proficient in developing and refining internet-scale search systems.Exhibit a strong sense of ownership over projects and are eager to acquire new skills to tackle challenges effectively.Demonstrate the ability to work swiftly in an environment with evolving parameters and competing priorities.

Join Firecrawl as a Research Engineer focusing on Search and Information Retrieval (IR). In this pivotal role, you will leverage cutting-edge technologies to develop innovative solutions that enhance our clients' search capabilities. You will work closely with cross-functional teams to analyze data, implement algorithms, and contribute to the advancement of our search platforms.

About Plaid Plaid builds tools that help developers create new financial products and experiences. Since 2013, Plaid has connected millions of users to over 12,000 financial institutions across the US, Canada, the UK, and Europe. The company partners with organizations like Venmo, SoFi, Fortune 500 firms, and major banks to make linking financial accounts to apps and services easier. Headquarters are in San Francisco, with offices in New York, Washington D.C., London, and Amsterdam. Team: Data Foundation & AI The Data Foundation and AI team designs and maintains the machine learning and AI infrastructure that supports Plaid’s products. This group transforms Plaid’s financial network data into flexible formats used by teams across the company. Responsibilities span the entire system lifecycle: data curation for pretraining, model development, deployment, serving, and monitoring in production. Role Overview: Senior Machine Learning Engineer (Research Scientist) This position focuses on applied research for Plaid’s foundation model. The Senior Research Scientist leads efforts to design model architectures, set pretraining objectives, and implement fine-tuning strategies that work across a range of product needs. The role also involves building and maintaining production machine learning systems, including training pipelines, model serving, feature engineering, and performance monitoring. Key Responsibilities Design model architectures and define pretraining objectives for Plaid’s foundation model Develop and apply fine-tuning methods for diverse product use cases Build and maintain end-to-end machine learning systems, from data pipelines to model serving Engineer features and monitor system performance in production Create evaluation frameworks to measure model quality across multiple tasks and metrics Location This role is based in San Francisco.

Join OpenAI as a Research Scientist and explore cutting-edge machine learning innovations. In this role, you will be at the forefront of developing groundbreaking techniques while advancing our team's research initiatives. Collaborate with talented peers across various teams to discover transformative ideas that scale effectively. We seek individuals who are passionate about pushing the boundaries of AI and want to contribute to our unified research vision.

OverviewBecome an integral part of our dynamic R&D team dedicated to developing fully automated research systems that push the boundaries of AI. Zochi has achieved a milestone by publishing the first entirely AI-generated A* conference paper. Locus has set a new industry standard as the first AI system to surpass human experts in AI R&D.Key ResponsibilitiesConceptualize and develop innovative architectures for automated research.Work collaboratively within a specialized team of researchers addressing cutting-edge challenges in long-horizon agentic capabilities, post-training for open-ended objectives, and environment crafting.Document and publish key internal findings alongside success stories from external collaborations.QualificationsPhD or equivalent research experience in Computer Science, Machine Learning, Artificial Intelligence, or a related discipline. Outstanding candidates with significant research contributions are encouraged to apply, regardless of formal qualifications.Demonstrated history of impactful AI/ML research contributions in academic or corporate environments.Expertise in developing long-horizon, multi-agent systems and/or model post-training, especially in scientific domains or for open-ended discovery objectives.A strong passion for advancing problem-solving processes and scientific discovery, thriving in high-autonomy roles and environments.Our CultureCompetitive compensation and equity options.Unlimited Paid Time Off (PTO), emphasizing team collaboration and a community-focused workplace.Opportunities for conference participation and engagement in community initiatives.Empowered roles with high levels of responsibility.#1: We are a small, passionate team of leading investors, researchers, and industry experts committed to the mission of accelerating discovery. Join us.

At Scale AI, we collaborate with leading AI laboratories to supply high-quality data and foster advancements in Generative AI research. We seek innovative Research Scientists and Research Engineers with a strong focus on post-training techniques for Large Language Models (LLMs), including Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and reward modeling. This position emphasizes optimizing data curation and evaluation processes to boost LLM performance across text and multimodal formats. In this pivotal role, you will pioneer new methods to enhance the alignment and generalization of extensive generative models. You will work closely with fellow researchers and engineers to establish best practices in data-driven AI development. Additionally, you will collaborate with top foundation model labs, providing critical technical and strategic insights for the evolution of next-generation generative AI models.

About AnthropicAt Anthropic, we are dedicated to developing AI systems that are reliable, interpretable, and steerable. We aim to ensure that AI is safe, beneficial, and aligned with the needs of both our users and society. Our expanding team consists of passionate researchers, engineers, policy experts, and business leaders collaborating to create groundbreaking AI solutions.About the RoleWe are seeking a talented Research Engineer with a solid foundation in computer vision, who shares our belief that visual and spatial reasoning are essential for unleashing the full potential of large language models (LLMs). In this collaborative role, you will engage in research, development, and evaluation of cutting-edge Claude models, with a specific emphasis on enhancing visual and spatial capabilities. You will contribute across multiple facets of our research initiatives, employing a full-stack approach that encompasses pretraining, reinforcement learning (RL), and runtime techniques such as agentic harnesses. Additionally, you will work closely with our product team to ensure that your vision enhancements positively influence Claude's performance in real-world applications.

abundant seeks a Research Lead based in San Francisco. This position steers research activities that help shape the company’s direction. The Research Lead partners with colleagues to analyze data, draw meaningful insights, and support projects where research has a clear business impact. Key responsibilities Plan, manage, and execute research initiatives from start to finish Work with team members to analyze data and spot important trends Turn research results into practical recommendations for the business Support projects that guide company strategy Collaboration and impact This role involves close teamwork and communication across departments. Research findings directly inform business decisions and contribute to the company’s ongoing growth.

Merge Labs is an innovative research facility dedicated to merging biological sciences and artificial intelligence to enhance human capability, autonomy, and experience. Our mission is to pioneer revolutionary methodologies in brain-computer interfaces that facilitate high-bandwidth interactions with the brain, seamlessly integrate advanced AI, and maintain safety and accessibility for all users.About the TeamAt Merge, we are addressing some of the most ambitious challenges in molecular engineering, synthetic biology, and neuroscience. Our Research Platform Team is responsible for creating the experimental frameworks necessary to tackle these challenges with exceptional speed and precision. The tools and methodologies developed by our team significantly enhance molecular assembly, protein expression, mammalian cell culture, advanced microscopy, sequencing, and unique custom techniques. We collaborate with program teams to establish and optimize these capabilities, implement automation where beneficial, and integrate with our data science and machine learning pipelines, continuously pushing the boundaries of throughput and innovation.About the RoleAs a Platform Scientist, you will be instrumental in developing high-efficiency and high-throughput experimental pipelines that accelerate research initiatives. You will work closely with program leads, project scientists, data scientists, and engineers, leading your work and potentially recruiting additional team members as necessary.Key Responsibilities:Collaborate with program leads and scientists to identify critical experimental requirements and workflows.Develop processes to facilitate high-throughput and/or high-efficiency experiments, including reagent production and analysis.Scope, procure, construct, program, and validate instruments to support experimental workflows.Ensure the quality, reliability, and integrity of data generated from automated pipelines, including defining and implementing suitable quality control checkpoints.Work alongside data science and machine learning engineers to incorporate metadata tracking, computational design, and analysis into experimental pipelines.Partner with electrical, mechanical, and software engineers to create custom setups.Innovate and validate concepts to enhance experimental throughput.

Join Our Team as a Research ScientistAt Parallel, we are at the forefront of web infrastructure innovation, enabling businesses across sectors such as sales, marketing, insurance, and technology to harness the power of AI. Our state-of-the-art products empower users to develop superior AI agents with seamless and flexible access to the web.With significant backing of $130 million from prominent investors like Kleiner Perkins, Index Ventures, and Spark Capital, we are dedicated to redefining the web for artificial intelligence. As we expand, we're assembling a top-tier team of engineers, designers, marketers, sales experts, researchers, and operational specialists committed to our vision.Your Role: As a Research Scientist, you will tackle the challenge of training and scaling models designed to enhance web indexing capabilities.About You: You possess a profound understanding of contemporary models and training methodologies. You enjoy engaging in discussions about the convergence of search, recommendations, and transformer models, and are passionate about translating your research into impactful products and systems utilized by millions.

Zyphra is a pioneering artificial intelligence firm located in the vibrant city of San Francisco, California.About the Role:We are seeking a passionate Research Scientist to join our dynamic Agency and Reasoning Team at Zyphra. In this role, you will conduct cutting-edge research in reinforcement learning, post-training methodologies, and human preference learning. Your innovative ideas will be instrumental in shaping our next-generation language models, enabling their application on a large scale.What We Desire:A strong sense of research intuition and tasteCapability to navigate a research project from initial concept to execution and documentationProficiency in implementation and prototypingA quick thinker who can rapidly transform ideas into experimental frameworksAbility to collaborate effectively in a fast-paced research environmentAn insatiable curiosity and enthusiasm for the study of intelligence.Qualifications:Proven experience and skill in reinforcement learning, particularly in the context of language model reasoning or classical RL tasksFamiliarity with language-model-supervised fine-tuning and preference-learning techniques, such as DPO and simPO.Experience with methods for context-length extensionStrong intuitive understanding of model behaviors, with the ability to refine them through iterative fine-tuningInterest in engaging deeply with data and dedicating time to data engineering and synthetic data generationA postgraduate degree in a scientific discipline (Computer Science, Electrical Engineering, Mathematics, Physics)Published research in reputable machine learning venuesExpertise in PyTorch and PythonEagerness and aptitude for rapidly acquiring new knowledge and implementing innovative conceptsExceptional communication and teamwork abilities, capable of contributing to both research and large-scale engineering effortsWhy Join Zyphra?We champion creative and unconventional ideas and are prepared to invest significantly in innovative concepts.Our culture fosters collaboration, curiosity, and intellectual growth.

Zyphra is a cutting-edge artificial intelligence firm headquartered in the vibrant city of San Francisco, California.Position Overview:As a Research Scientist specializing in Model Architectures, you will play a pivotal role in Zyphra’s AI Architecture Research Team. Your responsibilities will include the design and thorough evaluation of innovative model architectures and training methodologies aimed at enhancing essential modeling capabilities (e.g., loss per flop or loss per parameter) and tackling core limitations inherent in current models. You will collaborate closely with our pre-training team to ensure that your findings are seamlessly integrated into our next-generation models.Qualifications:A strong research acumen and intuition.Proven ability to navigate research projects from initial conception to execution and final write-up.Exceptional implementation and prototyping skills, with the capability to swiftly transform ideas into experimental outcomes.A collaborative spirit and the ability to thrive in a fast-paced research environment.A deep curiosity and enthusiasm for understanding intelligence.Requirements:Experience with long-term memory, RAG/retrieval systems, dynamic/adaptive computation, and alternative credit assignment strategies.Knowledge of reinforcement learning, control theory, and signal processing techniques.A passion for exploring and critically evaluating unconventional ideas, with the ability to maintain a unique perspective.Familiarity with modern training pipelines and the hardware necessities for designing efficient architectures compatible with GPU hardware.Strong understanding of experimental methodologies for conducting rigorous ablations and hypothesis testing.High proficiency in PyTorch and Python programming.Ability to quickly assimilate into large pre-existing codebases and contribute effectively.Prior publication of machine learning research in reputable venues.Postgraduate degree in a scientific discipline (e.g., Computer Science, Electrical Engineering, Mathematics, Physics).Why Join Zyphra?We emphasize a structured research methodology that systematically addresses ambitious challenges in AI.

Who We AreLightning AI, the innovative force behind PyTorch Lightning, was established in 2019 to create a seamless end-to-end platform for developing, training, and deploying artificial intelligence systems. Our mission is to facilitate the transition from research to production effortlessly.In partnership with Voltage Park, a leading neocloud and AI Factory, Lightning AI merges developer-centric software with optimized, large-scale computing solutions. We empower teams with the necessary tools for experimentation, training, and production inference while ensuring built-in security, observability, and control.We cater to individual researchers, emerging startups, and large enterprises alike. With a global presence, our offices are located in New York City, San Francisco, Seattle, and London, backed by top-tier investors including Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.

Join the Center for AI Safety (CAIS), a pioneering research and advocacy organization dedicated to addressing the societal-scale risks posed by artificial intelligence. We tackle the most pressing challenges in AI through rigorous technical research, innovative field-building initiatives, and proactive policy engagement, in collaboration with our sister organization, the Center for AI Safety Action Fund.As a Research Scientist, you will spearhead and conduct transformative research aimed at enhancing the safety and dependability of cutting-edge AI systems. Your responsibilities will include designing and executing experiments on large language models, developing the necessary tools for training and evaluating models at scale, and converting your findings into publishable research. You will work closely with CAIS researchers and external partners from academia and industry, utilizing our compute cluster for large-scale model training and evaluation. Your research will focus on critical areas such as AI honesty, robustness, transparency, and the detection of trojan/backdoor behaviors, all aimed at mitigating real-world risks associated with advanced AI technologies.

At World Labs, we are pioneering the development of Large World Models—advanced AI systems designed to comprehend, reason, and engage with the physical environment. Our initiatives are at the cutting-edge of spatial intelligence, robotics, and multimodal AI, with an objective to empower machines to perceive and operate effectively in intricate real-world settings.We are curating a global team of researchers, engineers, and innovators dedicated to transcending the existing boundaries of artificial intelligence. If you are eager to work on transformative technology that will redefine machine perception and enhance human-AI interaction, this role is tailored for you.About World Labs:World Labs is an AI research and development company committed to creating spatially intelligent systems capable of modeling, reasoning, and acting in the real world. We envision a future where AI transcends text or pixels to thrive in three-dimensional, dynamic environments, and we are constructing the foundational models that will make this a reality.Our team unites expertise in machine learning, robotics, computer vision, simulation, and systems engineering. We operate with the agility of a startup combined with the vision of a research lab, tackling long-term challenges that require creativity, rigor, and resilience.Our mission is to develop the most advanced world models and leverage them to empower individuals, industries, and society.Role Overview:We are seeking a Tech Lead for 3D Modeling & Reconstruction to establish technical direction and drive execution for our essential 3D modeling initiatives. This position is suited for someone with a strong background in research science (RS) or research engineering (RE) who has made significant contributions to the field of 3D reconstruction and/or modeling—evidenced by academic publications, widely adopted open-source projects, or large-scale production systems.This is a hands-on leadership role where you will merge profound technical expertise with the ability to guide a high-impact team, influencing both the research roadmap and the production systems that integrate contemporary 3D modeling techniques into tangible products. You will collaborate closely with research, engineering, and product teams to convert innovative concepts into dependable, scalable solutions.

About AfterQuery AfterQuery partners with leading AI labs to advance training data and evaluation frameworks. The team builds high-signal datasets and runs thorough evaluations that go beyond standard benchmarks. As a post-Series A, early-stage company in San Francisco, AfterQuery gives each team member room to shape the future of AI models. Role Overview: Research Scientist - Frontier Data This role focuses on designing datasets and developing evaluation systems that influence how top AI models are trained and assessed. Working closely with research teams at major AI labs, the scientist explores new data collection techniques, investigates where models fall short, and sets up metrics to track progress. The work is hands-on and experimental, moving quickly from hypothesis to live testing and directly impacting large-scale model training. Key Responsibilities Design data slides and analyze data structures to uncover model weaknesses in areas like finance, software development, and enterprise operations. Build and refine evaluation rubrics and reward signals for RLHF and RLVR training approaches. Study annotator behavior and run experiments to improve model capabilities across different domains. Develop quantitative frameworks to measure dataset quality, diversity, and their effect on model alignment and performance. Work with research teams to turn training objectives into concrete data and evaluation needs. What We Look For Experience as an undergraduate or master’s research student (PhD not required). Background or internships with RL environments or AI safety and benchmarking organizations (e.g., METR, Artificial Analysis) is a strong plus. Genuine interest in how data structure, selection, and quality affect model outcomes. Demonstrated skill in designing experiments, acting quickly, and extracting insights from complex data. Comfort working across sectors such as finance, software engineering, and policy. Strong quantitative background and familiarity with LLM training pipelines, RLHF/RLVR methods, or evaluation frameworks. A hands-on mindset focused on building practical solutions.